Assertional Simulation Using De-Referenced Data Models
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Solution Overview
Problem
Current systems for decision-making in business and analytics fail to effectively facilitate the presentation of alternative points of view due to limitations in handling different data structures and organizational formats, leading to inadequate support for modeling and simulation activities.
Innovation Solution
A system that configures a reference data set by removing semantic and structural constraints from source data, allowing for the creation of a de-referenced data model that isolates content and structure, and utilizes a recombinant access and mediation system to manage entity relationships and access rights, enabling the handling of alternative perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is organized according to source-specific structures and constraints, then data integrity and meaning are preserved, but the ability to present alternative points of view and perform flexible modeling is limited
Solution Approach 1:
The patent segments data into two distinct layers: a reference data model that preserves source-specific semantic constraints and relationships, and an assertional data model that enables flexible alternative viewpoints. This segmentation allows each layer to serve its specific purpose without compromising the other, resolving the contradiction between preserving information integrity and enabling adaptability.
Solution Approach 2:
The patent introduces an intermediary assertion model that acts as a mediator between the constrained reference data model and the need for flexible alternative viewpoints. This intermediary layer allows users to create and manipulate alternative perspectives without directly modifying or losing the original semantic constraints, thus maintaining both information integrity and adaptability.
2Adaptability or versatility
If multiple data structures and organizational formats are integrated, then comprehensive decision support is achieved, but system complexity increases
Solution Approach 1:
The patent creates a universal reference data model framework that can accommodate multiple source-specific data structures and organizational formats through standardized abstraction. This universal layer handles diverse data inputs while maintaining consistent semantic constraints, reducing the complexity that would otherwise arise from managing multiple specialized structures simultaneously.
Solution Approach 2:
The patent extracts source-specific structural constraints and semantic relationships from individual data sources and consolidates them into a centralized reference data model. This extraction process separates the complexity of handling diverse structures from the operational layer, allowing the system to manage multiple data formats without proportionally increasing overall system complexity.
3Reliability
If semantic constraints are enforced from source data structures, then data accuracy is maintained, but flexibility in modeling alternative scenarios is reduced
Solution Approach 1:
The patent implements a dynamic dual-model architecture where the reference data model maintains static semantic constraints for accuracy, while the assertional model provides dynamic flexibility for alternative scenario modeling. This dynamic separation allows the system to enforce data accuracy through constrained reference models while enabling flexible modeling through unconstrained assertional layers that can be freely modified for different scenarios.
Data Source
AI summary
Dereferencing comprises separating content of a source from structure of the source and separating content of the source from a meaning of the content within the structure.